Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Climate change may cause severe hydrological droughts, leading to water shortages which will require be assessed using high-resolution data. Gravity Recovery and Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution monitor drought, but its coarse resolution (1°) limits applications small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) Artificial Neural Network (ANN) downscale GRACE TWSA from 1° 0.25°. The findings revealed that XGBoost model outperformed ANN with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) Root Mean Square Error (RMSE) (5.22 mm), Absolute (MAE) (2.75 mm) between predicted GRACE-derived TWSA. Further, Deficit Index (WSDI) WSD (Water Deficit) were used determine severity episodes respectively. results WSDI exhibited strong agreement when compared Standardized Precipitation Evapotranspiration (SPEI) at different time scales (1-, 3-, 6-months) self-calibrated Palmer Drought Severity (sc-PDSI). Moreover, IBIS had experienced increasing drought episodes, e.g., eight detected within years 2010 2016 ?1.20 ?1.28 total ?496.99 mm ?734.01 mm, Partial Least Regression (PLSR) climatic variables indicated potential evaporation largest influence on after precipitation. this study helpful for drought-related decision-making in IBIS.